Safety-efficiency integrated assembly: The next-stage adaptive task allocation and planning framework for human–robot collaboration

Abstract

Human–robot collaboration (HRC) has sparked a new wave of the resilient, sustainable, and human-centric industrial revolution. In HRC-enabled manufacturing, safety is a constant focus since the traditional physical barriers between robots and humans are removed. Meanwhile, ensuring efficiency has also gained considerable attention with the rise in production demands and market competition. However, the integration and balance between these two critical prerequisites are overlooked in the task allocation and planning phase of the HRC assembly. In this paper, an adaptive task allocation and planning framework is proposed by simultaneously considering the safety and efficiency of HRC assembly. First, an execution time optimization strategy (ETO) is presented. This strategy adaptively adjusts safety paradigms in the spatio-temporal shared workspace. Thereby, the execution time of assembly tasks is well optimized while ensuring human–robot safety. Second, based on ETO and collaborative assembly task requirements, the safety-efficiency integrated assembly planning problem (SEAPP) is proposed and modeled. Third, compared to conventional single-objective HRC assembly planning problems, the solution space of SEAPP is significantly expanded, leading to a substantial increase in optimization complexity. Thus, a novel constrained multi-objective co-evolutionary algorithm, called GDCA, is proposed. By combining the complementary advantages of different evolutionary operators, GDCA enhances the diversity of solutions while ensuring convergence of optimization process. In comparison with variants that rely solely on a single evolutionary strategy, GDCA maintains better and more stable performance, validating the effectiveness of the co-evolutionary process. GDCA is also compared with four state-of-the-art constrained multi-objective optimization algorithms across a variety of SEAPP instances, widely spanning different task scales and durations. Across 20 assembly instances, GDCA shows superior $IGD$ over NSGA-II, PPS, BiCo, and MCCMO in 17, 19, 18, and 17 instances, and better $HV$ in 17, 19, 17, and 17 instances, respectively. Furthermore, the feasibility and effectiveness of the proposed SEAPF are validated by an industrial challenge in the HRC computer assembly. While ensuring safety, it significantly reduces the total assembly completion time, achieving a 6.2% time reduction over the SSM paradigm and 47.8% over the PFL paradigm, respectively.